(&mut self, vector: Vector)
| 112 | } |
| 113 | |
| 114 | fn insert_vector(&mut self, vector: Vector) { |
| 115 | if self.children.len() < 10 { |
| 116 | self.children.push(HierarchicalCluster::new( |
| 117 | vector, |
| 118 | self.max_size, |
| 119 | self.level + 1, |
| 120 | )); |
| 121 | self.levels_below = 1; // Update levels_below |
| 122 | self.update_centroid(); |
| 123 | return; |
| 124 | } |
| 125 | |
| 126 | let mut best_similarity = -1.0; |
| 127 | let mut best_child_idx = 0; |
| 128 | |
| 129 | for (idx, child) in self.children.iter().enumerate() { |
| 130 | let hypothetical_centroid = child.calculate_hypothetical_centroid(&vector); |
| 131 | let similarity = cosine_similarity(&vector, &hypothetical_centroid); |
| 132 | |
| 133 | if similarity > best_similarity { |
| 134 | best_similarity = similarity; |
| 135 | best_child_idx = idx; |
| 136 | } |
| 137 | } |
| 138 | |
| 139 | self.children[best_child_idx].insert_vector(vector); |
| 140 | // Update levels_below to be 1 + maximum levels among all children |
| 141 | self.levels_below = 1 + self |
| 142 | .children |
| 143 | .iter() |
| 144 | .map(|child| child.levels_below) |
| 145 | .max() |
| 146 | .unwrap_or(0); |
| 147 | self.update_centroid(); |
| 148 | } |
| 149 | |
| 150 | fn analyze_level(&self, target_level: usize, current_level: usize) -> Vec<usize> { |
| 151 | if current_level == target_level { |
no test coverage detected